We can solve various Knapsack problems using various evolutionary algorithms such as genetic ones. Of course, the solutions we get are not necessarily ideal, but in many situations we can be satisfied after some iterations of an evolutionary algorithm. Here I present an evolutionary algorithm in Python for solving this type of computational problems.
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The good news is we can use the Keras API for training deep models on Google Colaboratory TPU. But there are several important things we need to know in order to do so.
There’ve been proposed several types of ANNs with numerous different implementations for clustering tasks. Most of these neural networks apply so-called competitive learning rather than error-correction learning as most other types of neural networks do. ANNs used for clustering do not utilize the gradient descent algorithm.
Probably, the most popular type of neural nets used for clustering is called a Kohonen network, named after a prominent Finnish researcher Teuvo Kohonen.
There are many different types of Kohonen networks. These neural networks are very different from most types of neural networks used for supervised tasks. Kohonen networks consist of only two layers.